gpt-5.2GPT-5.2 is the newest frontier model in the GPT-5 lineup, with stronger agent abilities and long-context performance than GPT-5.1. It uses adaptive reasoning to stay fast on simple prompts while thinking more deeply on hard ones, and delivers steady gains across math, coding, science, and tool use — with more coherent long-form output and more reliable tooling.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="gpt-5.2", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="gpt-5.2",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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GPT-5.6 Luna Pro uses the same underlying model as GPT-5.6 Luna, but runs with reasoning.mode set to pro to deliver higher-quality responses on complex tasks. It is optimized for deeper reasoning, advanced coding, and multi-step agentic workflows, offering improved accuracy and solution quality while retaining the efficiency and scalability of the Luna tier.
GPT-5.6 Luna is the fast, cost-efficient model in OpenAI's GPT-5.6 series, optimized for high-volume, latency-sensitive workloads. It delivers capable reasoning at an affordable price point, making it ideal for chat applications, classification, and lightweight agentic workflows. Designed for scalable production deployments, GPT-5.6 Luna balances speed, cost, and reliability, providing efficient performance for real-time applications and large-scale automation tasks.
GPT-5.6 Terra Pro uses the same underlying model as GPT-5.6 Terra, but runs with reasoning.mode set to pro to deliver higher-quality responses on complex tasks. Optimized for deeper reasoning and greater reliability, it is well suited for advanced coding, multi-step reasoning, and agentic workflows where improved accuracy and solution quality are more important than maximizing speed or minimizing cost.
GPT-5.6 Terra is the balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is designed for everyday coding, reasoning, and agentic workflows, delivering strong performance while balancing capability and cost. Offering near-flagship quality at approximately half the cost of Sol, GPT-5.6 Terra is well suited for production applications that require reliable reasoning, software development, and scalable agent execution.
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GPT-5.4 Pro is OpenAI's most advanced model, built on the unified GPT-5.4 architecture with enhanced reasoning capabilities for complex and high-stakes tasks. It supports text and image inputs and features a 1M+ token context window (≈922K input, 128K output) for handling large-scale workflows and long-context analysis. Optimized for step-by-step reasoning, instruction following, and accuracy, GPT-5.4 Pro excels in agentic coding, long-context problem solving, and complex multi-step workflows, making it well suited for advanced engineering, research, and high-reliability applications.
gpt-4o-audio-preview adds support for audio inputs, allowing the model to understand nuances in audio recordings and enrich responses. It currently does not generate audio outputs, and audio input is billed per million audio tokens.
gpt-4o-mini-audio-preview is a smaller preview version of OpenAI’s audio-capable GPT-4o mini model that supports both audio and text inputs and outputs via the API. It enables the model to understand nuances in audio recordings and incorporate them into responses, making it useful for audio-enabled applications like transcription, speech understanding, and voice-driven interactions.
GPT-5.1 is the full-capability successor to GPT-5, offering stronger general reasoning, better instruction following, and a more natural conversational style. It uses adaptive reasoning to stay fast on simple questions while thinking more deeply on complex tasks, producing clearer, more grounded explanations. It shows steady improvements across math, coding, and structured analysis, with more coherent long-form output and more reliable tool use.
Initialized observational baseline with no recorded failures